For more than half a century, Direct Sequence Spread Spectrum (DSSS) technology has been widely used in both military and commercial communications. In DSSS, information data is spread by a pseudo-random (PN) sequence at the transmitter. At the receiver, the received signal is de-spread by an identical copy of the PN sequence to recover the original information data buried in the noisy received signal.
In this process, it is critical to align the code phase of the PN sequence as applied at the receiver with the code phase of the PN sequence embedded in the received signal. To achieve this goal, a preamble sequence is typically transmitted ahead of the information data. With a priori knowledge of the preamble sequence being transmitted, the preamble synchronization unit at the receiver can detect the arrival of the preamble and subsequently synchronize the code phase of the local PN sequence with that in the received signal.
The latest DSSS communication systems typically use concatenated sequences in the preambles to achieve fast and reliable synchronization. A concatenated sequence is formed by a Kronecker product between an inner code sequence and an outer code sequence. These code sequences have favorable periodic or aperiodic autocorrelation characteristics in that they are capable of suppressing correlation side-lobes.
Preamble synchronization operation at the receiver, as applied to the arrival concatenated preamble sequence, typically consists of a two-step correlation processing to accumulate signal energy and a subsequent synchronization detection processing to capture the preamble arrival occurrence from the correlation outputs. At the first correlation step, the received sequence passes through an inner code matched filter module which produces a train of correlation spikes corresponding to repetitive inner code sequences in the preamble. At the second correlation step, output from the inner code matched filter module passes through an outer code matched filter module. When the entire preamble sequence arrives, the train of correlation spikes matches the outer code matched filter coefficients and a correlation peak is generated. The preamble synchronization is detected by identifying the occurrence of this correlation peak. Several approaches have been proposed for correlation peak detection.
The first approach to capture the correlation peak is through signal energy differentiation. Specifically, the output signal from the outer code matched filter with time-varying amplitude is continuously tested against a threshold value. When the amplitude surpasses the threshold, the correlation peak is identified and preamble synchronization is declared. The threshold can be either set to a fixed value or adaptively adjusted based on statistical noise measurements. The theoretical Neyman-Pearson decision criterion is typically employed in choosing a threshold that keeps the probability of false alarms constant, as embodied in various Constant False Alarm Rate (CFAR) algorithms. The chosen threshold value 2σ2×loge(1/PFA) is essentially a function of the noise power estimate σ2 and the probability of false alarm PFA.
The majority of synchronization peak detection methods for the concatenated preamble sequence employ variants of the CFAR algorithms. These methods first measure the time-averaged amplitude of the output signal at the outer code matched filter, and then employ a simple peak-to-average ratio test to detect the correlation peak. The instantaneous peak-to-average ratio, which provides a rough estimate of the instantaneous signal-to-noise ratio (SNR) of the output signal samples, is expected to jump to its maximum value at the instance when the entire preamble sequence arrives in the correlation structure. Such synchronization peak detection schemes work well in most commercial applications in which the dynamic range of the SNR at the receiver is limited. The limited dynamic range of the SNR at the receiver makes it relatively easy to select a threshold for the peak-to-average ratio test.
Such synchronization peak detection schemes also work well in many existing military radios which are designed to have limited operational capabilities characterized in part by a limited set of spreading ratios. The limited set of spreading ratios confines the dynamic range of the SNR at the receiver and therefore permits the simple peak-to-average ratio test to effectively detect the synchronization peak.
Future radios promise to revolutionize military radio communications by providing a rich set of wireless communication capabilities within a single radio to accommodate various operational scenarios. A single radio is expected to provide a rich set of configurations, including a wide range of spreading ratios, a large set of configurable spectrum allocations, adjustable transmission duration for burst communication, and various data rates ranging from several Kbps to hundreds of Mbps. Future military radios also promise to operate in extremely variable wireless communication environments, which include jamming, severe channel fading, significant Doppler frequency shift and Doppler rate change, and various multi-path propagation delays, among other phenomena.
The wide range of radio operational configurations coupled with variable channel environments result in extremely wide dynamic ranges of both the SNR and the magnitude of the received signal. This makes it very difficult to select an appropriate threshold to satisfy all situations using the simple peak-to-average ratio test, given that the receiver does not have knowledge of operational configurations at the transmitter. If the threshold is set too low, correlation side-lobes may pass the threshold test and cause false alarms. If the threshold is set too high, true peaks may elude the peak-to-average ratio test and cause synchronization misses.
A widely-used solution that has been proposed is to set a conservative threshold, in order to avoid synchronization detection misses, then, after a threshold crossing event has occurred, employing an extended peak validation period to weed out false alarms. This solution partially alleviates the problem caused by the wide-dynamic-range SNR, but at the expense of increased synchronization detection time and the additional buffer space required to temporarily store information data that immediately follows the preamble sequence. Moreover, this solution does not work well for modern burst wireless communications which transmit information in short bursts and demand rapid synchronization detection.
A second approach to capture the correlation peak is through a hard-decision-aided two-step detection. The first step is the inner code detection, in which a train of correlation spikes corresponding to repetitive inner code sequences in the preamble in the preamble are set to “+1” by a hard-decision slicer while the rest of the sample are set to “−1”. In the second step, information associated with the bi-polar sequence is further processed in the outer code matched filter for final synchronization detection. The second step is further categorized into two subgroups based on which aspects of the hard-decision sequence information are being used in the second step. Methods in the first subgroup simply utilize the bi-polar value for correlation processing in the outer code matched filter. Methods in the second subgroup utilize the position index information of the “+1” samples in the hard-decision sequence and employ an index coincident decision rule for synchronization detection.
With the help of the hard-decision, the aforementioned second approach avoids the wide dynamic range problem that hinders the aforementioned first approach with signal energy differentiation. However, the second approach encounters its own problems, including poor performance under low SNR conditions due to the limited coherent energy accumulation, the poor probability of a false synchronization before the preamble signal arrives as well as the necessity to delay the peak detection decision by adding an extended peak validation period.
A third approach to capture the correlation peak is through the measuring of the threshold-crossing event statistics in a sliding window. If a certain number of threshold-crossing events occur in a sliding window, the correlation peak is detected. This approach has drawbacks similar to those of the aforementioned second approach.
Beyond their individual drawbacks, all three aforementioned synchronization peak detection approaches do not fully exploit the unique concatenated structure embedded in the received sequence. For example, in calculating the time-averaged amplitude in the peak-to-average ratio test, the periodic repeating signal structure is ignored and the signal component is leaked into the noise power estimation.